Mining activities will eventually deplete any deposit. In a sustainability perspective, the deposit should therefore be utilised optimally during production. A prerequisite to achieve this is the deliberate and consistent utilisation of the variations in the deposit. In an ideal world everything is certain. In the real world nothing is certain. In the real world everything is more or less probable. Therefore, the question asked is how an underground iron ore mining company like Rana Gruber AS can benefit from knowing and exploiting the uncertainty and variability of decisive ore parameters. The perspective is the value chain from in-situ ore to product, whereas the focus is on deposit characterisation and production. In order to answer this question the existing database with geodata from the Kvannevann Iron Ore is reviewed and estimation techniques based on kriging and geostatistical simulation algorithms (Turning Band) are implemented to identify and assess the ore deposit uncertainties and variations and associated risks. Emphasise is on total iron in the ore (FeTot), total iron in the ore originating from magnetite (FeMagn), manganese oxide (MnO) and joint parameters. Due to insufficient number of assays of MnO, a geochemical MnO-signature is developed using cluster analysis. This geochemical signature is applied as input in the kriging with inequalities procedure. This procedure is based on soft data (lithologies) and a conditional expectation of the MnO level in the different lithologies. A cut-off based on both hematite and magnetite is estimated. A process analysis is performed to visualise the working processes, related inputs, outputs and controlling-, supporting- and risk elements. The process analysis is based on the IDEF process modelling methodology. Given the identified deposit uncertainties and variations, systems to evaluate potential mining stope performance are developed and implemented for one of the mining stopes. To test the possibility to decrease the ore-related uncertainty, a method for collection of drill cuttings has been developed and tested. The correlation between magnetic susceptibility and FeMagn and the correlation between ore density and FeTot are both investigated. The results show that an illustrative and useful overview can be won by using the IDEF-based process modelling methodology. A non-linear relationship between density and FeTot is established and it is shown that the density can be used as a FeTot indicator. This relationship is also used in the reserve and resource estimation. As expected a positive correlation between FeMagn and magnetic susceptibility measured on cores could be established. However, the deviation from other reported relationships is considerable. The importance of magnetite is emphasised and quantified by the cut-off estimation. The cluster analysis reveals that the MnO levels in the different lithologies are significantly different. This result is implemented into the kriging with inequalities procedure and immediate effects can be observed. The development of the geodata collector and the collection of drill cuttings show that it is possible to obtain precise analysis of collected drill cutting material. Although high- and low assay values have been correlated with geological observation in the mine, the accuracy has been difficult to assess. The estimation and the simulation of the ore properties illustrate and quantify the uncertainties and variations in the ore deposit well. The structural analysis performed prior to the estimation and the simulation reveals anisotropies for all ore decisive parameters. The quantification of ore variations provides a useful input into the a-priori assessment of stope performance. It is also shown that the probability that a SMU is above or below some cut-off value can be assessed using the simulation results and the systems developed in standard software. It is concluded that the process analysis approach offers valuable input to gain an overview of the mining value chain. It is also an approach that constitutes an important step in the identification and assessment of IT-requirements, bottlenecks, input- and output requirements and role- and skill requirements along the value chain. However, the process analysis approach requires sufficient organisational resources, which also is the case regarding the implementation of the grade- and stability issues that are presented. Further it is concluded that the ore variations can be utilised to some extent by using standard software. The ore in question is a Neoproterozoic (600 to 700 Ma) metasedimentary magnetite-hematite ore deposited under shallow marine conditions. Primary precipitate was probably ferric hydroxide. Applied methods have been chosen to handle the uncertainty along the value chain of Rana Gruber AS. Every aspect of these methods may therefore not be directly applicable to other mining operations. However, the general aspects have a broad area of use.

The International Atomic Energy Agency (IAEA) and the World Health Organisation (WHO) operate the IAEA/WHO TLD postal dose audit programme. The purpose of the programme is to verify the beam calibration in radiotherapy centres in developing countries and to check the Secondary Standards Dosimetry Laboratories (SSDLs). Thermoluminescence dosimeters (TLDs) are used as transfer dosimeters and the evaluation of these are done at the IAEA Dosimetry Laboratory. In the present work the uncertainties in the process of dose determination from TLD readings have been evaluated. The analysis comprises the TLD reading reproducibility, uncertainties in the calibration coefficient, and uncertainties in factors correcting for fading of TL signal, influence of TLD holder, energy response and dose response non-linearity. The individual uncertainties were combined to estimate the total uncertainty in the evaluated dose from TLD readings. Experimental data from 2001-2005 were used in the analysis. The total uncertainty has been estimated to be 1.2 % for irradiations with 60Co -rays and 1.6 % for irradiations with high-energy X-rays. Results from irradiations by the Bureau International des Poids et Mesures (BIPM), Primary Standard Dosimetry Laboratories (PSDLs), Secondary Standard Dosimetry Laboratories (SSDLs) and reference centres compare favourably with the estimated uncertainties. The largest uncertainty components are in the energy correction factor (for high-energy X-rays) with a value of 1.1 % and in the dose response non-linearity correction factor with a value of 0.9 %. It has been shown that the acceptance limits of 5 % for TLD results of hospitals and 3.5 % for SSDLs are justified when related to the uncertainties in the dose calculations and the uncertainty in the determination of absorbed dose to water at the centre, as discussed in IAEA TRS-398 (IAEA, 2000), provided that it is followed.

The role of purchasing in supply chain management has received and continues to receive increasing attention as the years go by. Purchasing enhances efficiency and competitiveness among other benefits but to realize these benefits it is imperative to select and maintain competent suppliers. However, many factors affect a firm’s ability to choose the right supplier. Uncertainty is an issue that has received great attention. It affects all functions of a company consequently affecting purchasing and supplier selection. This thesis seeks to provide an understanding of the supplier selection process and criteria under circumstances of uncertainty in the case where the potential supplier under evaluation is a newly created company. The authors try to find out if uncertainty varies with firm’s age and tested the suitability of existing criteria on the selection of newly created firms. They also sought ways by which uncertainty can be reduced.One of the realisations of this thesis is that there is a relationship between the characteristics or problems faced by new firms and uncertainty. Uncertainties created by new firms include lack of trust and commitment, inadequate finance, poor quality, unreliable delivery times, inadequate logistic technological capabilities. No new types or sources of uncertainty were discovered however, it was found that uncertainty was certainly higher when working with new firms. The criteria delivery, quality, cost/price, financial position and communication and technology were recognized as the commonly used criteria a fact confirmed from empirical results as well as in previous literature. However other criteria such as ISO certification, reliability, credibility, good references and product development were also identified. These criteria had existed before but did not receive the same attention in previous studies. This show that focus is shifting from solely relying on quantitative factors to include qualitative criteria. The study identified that some methods of minimising uncertainty could include detailed financial analyses, site visit, intensive verification, close relationships, ISO certification, good references and recommendations. It is worth noting that uncertainty cannot be entirely eliminated in all situations

In Black-Scholes model, the parameters -a volatility and an interest rate were assumed as constants. In this thesis we concentrate on behaviour of the volatility as a function and we find more realistic models for the volatility, which elimate a risk connected with behaviour of the volatility of an underlying asset. That is the reason why we will study the Uncertain Volatility Model. In Chapter 1 we will make some theoretical introduction to the Uncertain Volatility Model introduced by Avellaneda, Levy and Paras and study how it behaves in the different scenarios. In Chapter 2 we choose one of the scenarios. We also introduce the BSB equation and try to make some modification to narrow the uncertainty bands using the idea of a static hedging. In Chapter 3 we try to construct the proper portfolio for the static hedging and compare the theoretical results with the real market data from the Stockholm Stock Exchange.

The purpose of this study is to investigate how individual retirement savings are made tangible for potential customers. As case companies we have studied the marketing strategies of Handelsbanken and Swedbank. We use Miller and Foust’s classification of services by intangibility based on attributes and benefits and Tarn’s four strategy marketing-based model in order to find out how the retirement savings are made tangible. We find that the attributes and benefits that are used to define retirement savings depend on how the service is affiliated to life-insurance or banking services. Intangibility is coped by simplifying the products by lifting forward salient features. Complexity is overcome by explanation. Putting emphasis on flexibility is a mean to cope with the uncertainty of future income.

The internationalization process is usually viewed from an outward- perspective ininternational business literature. In this thesis the focus is put on an outward-inwardinterrelationship perspective. An extensive literature review from literature in internationalbusiness literature, entrepreneurial literature as well as international marketing literature wasconducted in order to extract key variables that affect the process of internationalization of thefirm. The key variables that were examined were: Business Contacts, InternationalExperience, Perceived International Opportunity and Perceived International Uncertainty.A qualitative interview with semi-structured questions was conducted in order to construct amodel that should be used as the basis for a quantitative study of an inward-driveninternationalization process, in order to generalize. The results were in line with thepropositions based on the relationships between the variables. In other words, the constructedmodel and the developed questionnaire were of use when examining the relationship betweenthe variables and seem to be of value when applied to an inward oriented context. Thevariable International Experience received the highest score and seemed to reflect animportant contribution for the internationalization process. Nevertheless, further quantitativeresearch is asked for, in order to support the results and be able to generalize.

The problem of handling dependent evidence is an important practical issue for applications of reasoning with uncertainty in artificial intelligence. The existing solutions to the problem are not satisfactory because of their ad hoc nature, complexities, or limitations. In this dissertation, we develop a general framework that can be used for extending the leading uncertainty calculi to allow the combining of dependent evidence. The leading calculi are the Shafer Theory of Evidence and Odds-likelihood-ratio formulation of Bayes Theory. This framework overcomes some of the disadvantages of existing approaches. Dependence among evidence from dependent sources is assigned dependence parameters which weight the shared portion of evidence. This view of dependence leads to a Decomposition-Combination method for combining bodies of dependent evidence. Two algorithms based on this method, one for merging, the other for pooling a sequence of dependent evidence, are developed. An experiment in soybean disease diagnosis is described for demonstrating the correctness and applicability of these methods in a domain of the real world application. As a potential application of these methods, a model of an automatic decision maker for distributed multi-expert systems is proposed. This model is a solution to the difficult problem of non-independence of experts. / Graduation date: 1991

Essay one develops and tests a revenue-maximizing tax structure model. This model represents one of the first attempts to evaluate and compare the responsiveness of various tax instruments to tax evasion within a tax revenue maximization framework. We use data from both the OECD and East African countries and estimation is via a seemingly unrelated regression model. The GDP share of agricultural income is used as an instrument to correct for the simultaneity between tax revenue shares and tax evasion. Our findings indicate that tax evasion increases the tax authority’s reliance on consumption taxes vis-à-vis taxes on income, suggesting that diverse tax instruments respond differently to tax evasion, and as such the choice of a revenue-maximizing tax structure is influenced by the amount of revenue lost through tax evasion. Essay two analyzes the incidence of tax evasion in both the formal and informal sectors of the economy using a computable general equilibrium model. This essay incorporates the element of uncertainty in an individual’s decision to evade so as to account for the uncertainty of returns to the tax evader. We also allow for varying degrees of competi¬tion or entry across sectors in the economy to examine how much of the tax advantage is retained by the initial evaders and how much is shifted via factor and commodity price changes. Our simulation results show that the evading households’ post-evasion welfare is only 0.68-3.40 percent higher than the post-tax welfare if it had fully complied with taxes. The simulation results further reveal that the evading household keeps 77.1-83.2 percent of this initial increase in welfare, while 16.8-22.9 percent of this initial gain is competed away as a result of increased competition and entry into the informal sector. The compliant households’ welfare increases by 58.8-101.7 percent with increased competition in the informal sector. Therefore, if we construe the changes in consumer welfare as an overall indicator of the gains and/or losses from tax evasion, then the evading household only benefits marginally and this advantage diminishes with increased entry or competition in the informal sector.

Several studies indicate that the data-driven models have proven to be potentially useful tools in hydrological modeling. Nevertheless, it is a common perception among researchers and practitioners that the usefulness of the system theoretic models is limited to forecast applications, and they cannot be used as a tool for scientific investigations. Also, the system-theoretic models are believed to be less reliable as they characterize the hydrological processes by learning the input-output patterns embedded in the dataset and not based on strong physical understanding of the system. It is imperative that the above concerns needs to be addressed before the data-driven models can gain wider acceptability by researchers and practitioners.<p>In this research different methods and tools that can be adopted to promote transparency in the data-driven models are probed with the objective of extending the usefulness of data-driven models beyond forecast applications as a tools for scientific investigations, by providing additional insights into the underlying input-output patterns based on which the data-driven models arrive at a decision. In this regard, the utility of self-organizing networks (competitive learning and self-organizing maps) in learning the patterns in the input space is evaluated by developing a novel neural network model called the spiking modular neural networks (SMNNs). The performance of the SMNNs is evaluated based on its ability to characterize streamflows and actual evapotranspiration process. Also the utility of self-organizing algorithms, namely genetic programming (GP), is evaluated with regards to its ability to promote transparency in data-driven models. The robustness of the GP to evolve its own model structure with relevant parameters is illustrated by applying GP to characterize the actual-evapotranspiration process. The results from this research indicate that self-organization in learning, both in terms of self-organizing networks and self-organizing algorithms, could be adopted to promote transparency in data-driven models.<p>In pursuit of improving the reliability of the data-driven models, different methods for incorporating uncertainty estimates as part of the data-driven model building exercise is evaluated in this research. The local-scale models are shown to be more reliable than the global-scale models in characterizing the saturated hydraulic conductivity of soils. In addition, in this research, the importance of model structure uncertainty in geophysical modeling is emphasized by developing a framework to account for the model structure uncertainty in geophysical modeling. The contribution of the model structure uncertainty to the predictive uncertainty of the model is shown to be larger than the uncertainty associated with the model parameters. Also it has been demonstrated that increasing the model complexity may lead to a better fit of the function, but at the cost of an increasing level of uncertainty. It is recommended that the effect of model structure uncertainty should be considered for developing reliable hydrological models.

This study investigates the influence that newspaper news and other determinants exercise on the decision-making of executives and hence on the expansion strategies and the performance of medium-sized companies in the Italian food sector. This sector is characterized by companies that have a turnover between 10 and 50 million Euros and, for the bigger ones in this range, around 100 and 200 employees. The use of interviews (questionnaires) and secondary data, combined with a news gathering process are adopted by the authors in order to describe how managers respond to the information coming from newspapers and what other types of knowledge (or intangible assets) there are that can help to downplay the increasingly negative reports of the general downturn in the Italian economy. Evidently, as these companies show growth in revenues and a tendency to adopt exporting as the main strategy to go abroad, newspaper reports seem not to have an influence on managers’ decisions whereas know-how, instinct and personal experience are considered important factors, crucial for the achievement of the companies’ success.